Literature DB >> 27631425

Graphics Processing Unit Acceleration and Parallelization of GENESIS for Large-Scale Molecular Dynamics Simulations.

Jaewoon Jung1,2, Akira Naurse3, Chigusa Kobayashi2, Yuji Sugita1,2,4,5.   

Abstract

The graphics processing unit (GPU) has become a popular computational platform for molecular dynamics (MD) simulations of biomolecules. A significant speedup in the simulations of small- or medium-size systems using only a few computer nodes with a single or multiple GPUs has been reported. Because of GPU memory limitation and slow communication between GPUs on different computer nodes, it is not straightforward to accelerate MD simulations of large biological systems that contain a few million or more atoms on massively parallel supercomputers with GPUs. In this study, we develop a new scheme in our MD software, GENESIS, to reduce the total computational time on such computers. Computationally intensive real-space nonbonded interactions are computed mainly on GPUs in the scheme, while less intensive bonded interactions and communication-intensive reciprocal-space interactions are performed on CPUs. On the basis of the midpoint cell method as a domain decomposition scheme, we invent the single particle interaction list for reducing the GPU memory usage. Since total computational time is limited by the reciprocal-space computation, we utilize the RESPA multiple time-step integration and reduce the CPU resting time by assigning a subset of nonbonded interactions on CPUs as well as on GPUs when the reciprocal-space computation is skipped. We validated our GPU implementations in GENESIS on BPTI and a membrane protein, porin, by MD simulations and an alanine-tripeptide by REMD simulations. Benchmark calculations on TSUBAME supercomputer showed that an MD simulation of a million atoms system was scalable up to 256 computer nodes with GPUs.

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Year:  2016        PMID: 27631425     DOI: 10.1021/acs.jctc.6b00241

Source DB:  PubMed          Journal:  J Chem Theory Comput        ISSN: 1549-9618            Impact factor:   6.006


  6 in total

Review 1.  Whole-Cell Models and Simulations in Molecular Detail.

Authors:  Michael Feig; Yuji Sugita
Journal:  Annu Rev Cell Dev Biol       Date:  2019-07-12       Impact factor: 13.827

2.  Scaling molecular dynamics beyond 100,000 processor cores for large-scale biophysical simulations.

Authors:  Jaewoon Jung; Wataru Nishima; Marcus Daniels; Gavin Bascom; Chigusa Kobayashi; Adetokunbo Adedoyin; Michael Wall; Anna Lappala; Dominic Phillips; William Fischer; Chang-Shung Tung; Tamar Schlick; Yuji Sugita; Karissa Y Sanbonmatsu
Journal:  J Comput Chem       Date:  2019-04-17       Impact factor: 3.376

3.  Modified Hamiltonian in FEP Calculations for Reducing the Computational Cost of Electrostatic Interactions.

Authors:  Hiraku Oshima; Yuji Sugita
Journal:  J Chem Inf Model       Date:  2022-05-31       Impact factor: 6.162

4.  Hybrid All-Atom/Coarse-Grained Simulations of Proteins by Direct Coupling of CHARMM and PRIMO Force Fields.

Authors:  Parimal Kar; Michael Feig
Journal:  J Chem Theory Comput       Date:  2017-10-19       Impact factor: 6.006

5.  New parallel computing algorithm of molecular dynamics for extremely huge scale biological systems.

Authors:  Jaewoon Jung; Chigusa Kobayashi; Kento Kasahara; Cheng Tan; Akiyoshi Kuroda; Kazuo Minami; Shigeru Ishiduki; Tatsuo Nishiki; Hikaru Inoue; Yutaka Ishikawa; Michael Feig; Yuji Sugita
Journal:  J Comput Chem       Date:  2020-11-16       Impact factor: 3.376

6.  Crowding in Cellular Environments at an Atomistic Level from Computer Simulations.

Authors:  Michael Feig; Isseki Yu; Po-Hung Wang; Grzegorz Nawrocki; Yuji Sugita
Journal:  J Phys Chem B       Date:  2017-07-12       Impact factor: 2.991

  6 in total

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